Gotowa bibliografia na temat „Freight-truck route choice”

The adverse impacts of greenhouse gasses (GHG) and the imperative for reducing the existing rate of GHG production are well established. In the United States, the largest source of GHG emissions from human activities is from burning fossil fuels, primarily for the generation of electricity and transportation. The transportation sector accounts for 28% of all U.S. GHG production. Heavy-duty vehicles, such as large freight trucks, account for nearly one-fifth of the U.S. total, and this fraction is expected to grow rapidly. Consequently, many efforts are being used to reduce the total emissions of freight trucks. Most efforts emphasize one of four areas: engineering improvements to improve fuel economy or reduce emissions, shifts to other transport modes, improved logistics to reduce the movement of partially full or empty containers, and reduced travel costs for individual trucks. A few studies have assessed modifications to route choice considerations as a means of improving the fuel economy of individual vehicles and show potential gains. In this study, the potential gains of emissions-based route choice were assessed by integrating the U.S. Environmental Protection Agency motor vehicle emission simulator with a macroscopic regional traffic demand model. For this integration, route choices included a simplified emissions calculation within the repeated model iteration runs of an algorithm of the Frank–Wolfe type. The analyses suggested that reductions of freight truck emissions were possible and showed an example in which the total system's truck emissions were reduced by up to 0.61% (88.8 tons).

Optimizing route choices for truck drivers is a key element in achieving reliable road freight operations. For commercial reasons, it is often difficult to collect freight activity data through traditional surveys. Automated vehicle identification (AVI) data on fixed locations (e.g., Bluetooth or camera) are low-cost alternatives that may have the potential to estimate route choice models. However, in cases where these AVI sensors are sparsely located, the resulting data lack actual route choices (or labels), which limits their application estimating route choice models. This paper overcomes this limitation with a new two-step approach based on fusing AVI and loop-detector data. First, a sparse Bluetooth data set is fused with travel times estimated from densely spaced loop-detector data. Second, the combined data set is fed into a bi-objective optimization method which simultaneously infers the actual route choices of truck drivers between an origin–destination pair and estimates the parameters of a route choice (discrete choice-based) model. We apply this approach to investigate the route choice behavior of truck drivers operating to and from the port of Rotterdam in the Netherlands. The proposed model can distinguish between peak and off-peak periods and identify different segments of truck drivers based on a latent classes choice analysis. Our results indicate the potential of traffic and logistics interventions in improving the route choices of truck drivers during peak hours. Overall, this paper demonstrates that it might be possible to estimate route choice characteristics from readily available data that can be retrieved from traffic management agencies.

Route choice models (or path choice models) are useful for quantifying travellers’ preferences for or sensitivity to route attributes, predicting network-level traffic flows, examining the influence of information provided to travellers, and studying travellers’ adaptation to uncertainty in travel conditions. Among the various factors influencing route choice, variability in travel conditions is an influential one. Day-to-day and within-day variations in travel conditions influence route choice decisions in many geographical contexts. Empirical studies on values of time and reliability have concluded that travellers, besides being interested in minimizing their travel times, also wish to minimize their travel time variability. The influence of travel time variability on route choice becomes more important in the context of freight transportation and logistics where delays due to uncertainty translate to large financial losses. Therefore, it is useful to quantify variability in travel conditions and to understand the influence of such variability on freight route choice decisions. This thesis proposes an Integrated Choice and Latent Variable (ICLV) modelling framework that allows simultaneous estimation of route-level travel time variability and incorporation of the influence of such variability on travel route choice of freight-trucks. The proposed framework considers the travel time on a route as a latent (unobserved) variable and uses GPS data measurements of route-level travel time to identify the parameters of its statistical distribution. Since such measurements are not always available for all routes, the latent variable component of the ICLV framework helps impute or inform the travel time distribution for routes without travel time measurements. In this regard, simultaneous estimation of the measurement and choice components of the proposed model allows the use of partial measurement data for estimation of travel time variability as well as incorporation of the influence of travel time variability on route choice. Further, route-level travel time variability is viewed as a result of variability in travel conditions (e.g., variability of travel speeds on links, etc.) and is captured through random coefficients on the route attributes specified in the latent variable model. The proposed model is applied to an empirical data set on truck route choice using truck-GPS data collected in the Tampa Bay region of Florida, USA. The empirical parameter estimates suggest that the variability of travel time on a route depends on the network structure along the route, such as the lengths of different roadway types, largely due to differences in variability of travel speeds among different types of roadways. The empirical findings indicate a superior statistical model fit of the proposed ICLV model than the traditional choice models that do not consider the influence of travel time variability on route choice. Although the ICLV model in this study was applied to the empirical context of freight-truck route choice, the proposed framework is applicable to accommodate the influence of variability in travel conditions on other travel choices such as transit route choice and travel mode choice; thanks to the increasing ubiquity of passively collected data on travel time (such as GPS data).

To contribute to the understanding of freeway capacity and financing options, this study evaluates the demand for truck-only toll lanes on Southern California freeways. The study implemented surveys to both company truck drivers and owner-operator truck drivers to estimate the value they place on time, reliability, and safety measures. The research team met face-to-face with both types of truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 45 surveys out of 62 survey responses were used for statistical analysis. The results showed that the tolerated toll fees that both types of truck drivers combined were willing to pay ranged from $3.27 an hour to $41.45 an hour with an average of $20.50 an hour during weekdays, while those fees ranged from $3.04 an hour to $36.12 an hour with an average of $18.12 an hour during weekends. Both types of truck drivers are unwilling to pay toll fees for the routes used in six comparisons out of nine, despite sharing a common origin and destination. Data shows that, regardless of ownership type, both types of truck drivers similarly value a route with truck only lanes. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of travel time (VOT) is $0.54 per mile or $32.38 an hour. The figures for the value of reliability (VOR) and safety measures are $0.47 per mile or $15.76 an hour and $0.17 per mile or $9.80 an hour, respectively. The VOR is important because it helps shippers and freight carriers make predictable travel times to remain competitive. These results are meaningful for legislators and transportation agencies because the behaviors and route choice characteristics of both types of drivers help them better reduce scheduling costs, understand the utility and demand for truck-only toll lanes, and resolve traffic congestion in the study area.

This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and $12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or $18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or $8.94/hr and $0.22/mile or $11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes.

Recent development of autonomous and connected trucks (ACT) has provided the freight industry with the option of using truck platooning to improve fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterioration and increases the life cycle costs for the highway agency. Also, effectiveness of truck platooning can be maximized only in dedicated lanes; and its benefits and costs need to be properly balanced between stakeholders. This paper proposes a network-design model to optimize (i) placement of dedicated truck-platoon lanes and toll price in a highway network, (ii) pooling and routing of ACT traffic from multiple origins and destinations to utilize these lanes, and (iii) configuration of truck platoons within these lanes (e.g., lateral displacements and vehicle separations). The problem is formulated as an integrated bi-level optimization model. The upper level makes decisions on converting existing highway lanes into dedicated platoon lanes, as well as setting user fees. The lower-level decisions are made by independent shippers regarding the choice of routes and use of platoon lanes vs. regular lanes; and they collectively determine truck traffic in all lanes. Link-cost functions for platoon lanes are obtained by simultaneously optimizing, through dynamic programming, pavement-rehabilitation activities and platoon configuration in the pavement's life cycle. A numerical case study is used to demonstrate the applicability and performance of the proposed model framework over the Illinois freeway system. It is shown that the freight traffic is effectively channelized on a few corridors of platoon lanes and, by setting proper user fees to cover pavement-rehabilitation costs, systemwide improvements for both freight shippers and highway agencies can be achieved.